At the core of our proposed methodology is the notion of learning low-dimensional embeddings of graph-structured data, especially social data.
Graph embedding techniques have a long history in the social sciences, with connections to early research on “sociograms” (small hand-constructed social networks) and latent variable models of social interactions [1]}, [2]}.
In more recent years, the task of embedding graph-structured data has received increasing attention from the machine learning and data mining communities [3]}, [4]}.
Generally, the goal of these works is to map graph nodes to low-dimensional vector embeddings, such that the original graph can be reconstructed from these embeddings.
Traditional approaches to this problem include Laplacian eigenmaps [5]} and matrix factorization techniques [6]}, with recent years witnessing a surge in methods that rely on random-walk based objectives [7]}, [8]}, deep autoencoders [9]}, and graph neural networks [10]}, [11]}.
